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A scalable RC architecture for mean-shift clustering

机译:用于均值漂移聚类的可扩展RC体系结构

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摘要

The mean-shift algorithm provides a unique non-parametric and unsupervised clustering solution to image segmentation and has a proven record of very good performance for a wide variety of input images. It is essential to image processing because it provides the initial and vital steps to numerous object recognition and tracking applications. However, image segmentation using mean-shift clustering is widely recognized as one of the most compute-intensive tasks in image processing, and suffers from poor scalability with respect to the image size (N pixels) and number of iterations (k): O(kN
机译:均值平移算法为图像分割提供了独特的非参数和无监督聚类解决方案,并且对于多种输入图像都具有非常好的性能证明。它对图像处理至关重要,因为它为众多对象识别和跟踪应用程序提供了重要的初始步骤。但是,使用均值漂移聚类的图像分割被广泛认为是图像处理中计算量最大的任务之一,并且相对于图像大小(N个像素)和迭代次数(k)而言,其可伸缩性较差:O(千牛

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